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Smart Industry Adoption: how analytics affect
SMEs in the Netherlands
Author: Regina Na-Young Chang University of Twente
P.O. Box 217, 7500AE Enschede The Netherlands
ABSTRACT,
Over the past years, “big data” has received a great deal of attention and was
found extremely important for companies as it gave imperative insights that could,
for example, help gain better understanding of customer needs. As the need for
and recognition of understanding big data grew, several frameworks were
developed to detect the maturity level of analytics and indicate improvements of
sophisticating current analytic tools. Even though SMEs are important drivers for
the economy and technological change, it became a question whether these
maturity level of analytics framework aided them in navigating the right analytic
direction. This paper aims to cognise “big data” and the maturity level of analytics,
to observe and provide arguments on whether the current maturity level analytics
frameworks can provide assistance to SMEs through data collection.
Graduation Committee members:
1st Supervisor: Dr. A.B.J.M. Wijnhoven
2nd Supervisor: Dr. R.P.A. Loohuis, MBA
Keywords Smart Industry, Big data, Analytics, Maturity levels, SME, Industry 4.0
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that
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11th IBA Bachelor Thesis Conference, July 10th, 2018, Enschede, The Netherlands. Copyright 2018, University of Twente, The Faculty of Behavioural, Management and Social sciences.
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1. INTRODUCTION Smart Industry is a development that is influenced by networks,
latest manufacturing technologies, information and digitisations
that improve and increase several factors such as quality,
flexibility, automation, the participation of the value chain and
importantly, enhance interaction with customers. It is aided by a
network-centric approach, making use of information and
appreciate its value, directed by ICT and upcoming
manufacturing techniques. Smart Industry Adoption also
includes Industry, Internet of Things, Analytics and “Big Data”,
they are all interrelated. These technological developments
introduce what has been called the “smart factory,” in which
cyber-physical systems monitor the physical processes of the
factory and make decentralised decisions (VID/VDE, 2015). The
physical processes systems in Industry 4.0 is in direct relation to
the Internet of Things which is the concept that is “basically
connecting any device with an on and off switch to the Internet
(and/or to each other)” (Morgan, 2014). Analytics is a
comprehensive and complex field that ‘involves statistical
analysis, computational linguistics, and machine learning’ to find
meaningful patterns and knowledge in recorded data (Gandomi
& Haider, 2015). It is used to understand “big data” which is a
large amount of data that overwhelms business on a daily basis.
However, quantity is not of main importance but rather how
business interacts with this data.
2. BACKGROUND OF SMART
INDUSTRY As Zheng et al., (2018) point out, “These technologies are
permeating the manufacturing industry and make it smart and
capable of addressing current challenges” (p. 1). Typically, the
characteristics of these challenges in Smart Industry is having
flexible production capacity in terms of products
(specifications, quality and design), volume, timing and
resources and cost efficiencies. Smart industry makes it
possible, to enhance the competitive advantage of the
organisation. Through research, several frameworks of how
Smart Industry affect businesses were found. They seem to
have in common that there are three layers (see figure 1, 2 & 3
in the appendix) of the value chain that interacts with each
other. These three layers are (1) Cloud systems, (2) Industrial
Networks (3) and Physical Resources. There is a recurring
theme that proves that these dimensions constitute the impact
Smart Industry has on businesses. Through cooperation with
each other, the layers align their own behaviours in order to
approach a common goal within the system. For example, there
is a demand for a customised product. The big data analytics
block knows this and transfers its knowledge towards the
coordination technologies. The coordination technologies then
help the manufacturing assets to interpret this data. The
manufacturing assets, in turn, try to achieve (for example)
maximum efficiency & quality. With feedback data, the loop
transfers back to analytics which tries to optimise the data
again. (B. Chen et al., 2018; Lee, Kao, & Yang, 2014; Wang,
Wan, Zhang, Li, & Zhang, 2016)
2.1 Layers of Smart Industry
2.1.1 Cloud Systems Cloud systems are the big data analytics assisting downstream
self-organisation and upstream supervisory control and should be
capable to analyse the semantics of various data (Wang et al.,
2016). Thus, there are great similarities between the cloud
systems and analytics, as it is the data forecasting analysis that
involves using historical data to research potential future
scenarios and help companies gain knowledge to make
improvements/changes. In addition, it analyses the effects of a
certain decision that could potentially harm or benefit the
company. Additionally, data mining could be used to ensure
design optimization and active maintenance. This dimension is
the catalyst of the other two dimensions.
2.1.2 Industrial Networks Industrial Networks is the backbone of the systems architecture,
providing efficient data exchange and controllability.
Technologies in this layer ensure reliable communication and
cooperation among equipment (B. Chen et al., 2018). Therefore,
this dimension includes all the technologies that assist the
manufacturing dimension. Some examples are toolkits, sensors,
3D Printing, drones, advanced robotics, etc. Besides, it is worth
mentioning the Intelligent transportation systems. It is an
application of “sensing, analysis, control and communication
technologies” that can increase transport safety, mobility and
efficiency and has a notable effect on transportation in
applications such as traveller information systems and traffic
signal control (Rouse, n.d.). Another coordinating technology is
value chain control towers. They monitor, manage, and control
decision-making across several functions and even companies.
All these technologies have importance in Smart Industry. We
will call these technologies coordination technologies. They
improve the value chain and manufacturing process efficiency
via support of analytics to help create a more profitable value
chain.
2.1.3 Physical Resources Physical resources are the assets that organisations use in the
process of manufacturing. Manufacturing is the process of
transforming raw materials and components into an end product
that fulfils the specifications and needs of the customer. The
process usually consists of many assets in a large-scale operation.
With the use of Coordination technologies fed by Analytics, the
process could gain in terms of quality, turnover time, cost
efficiencies and many more dimensions. Therefore, it is
important that the physical equipment gets real-time data and the
devices in the other layers have to provide it with ‘heterogeneous
and high-speed information’. (B. Chen et al., 2018).
These three layers are, as you can see, different, but do constitute
one cyber-physical system. Also, these three layers will all be
measured in a different way as can be seen with the
operationalised variables in the methodology part. In figure 1.1
we see after researching for literature and reviewing the
frameworks, it was agreed to create our own framework
influenced by the information found:
Large organisations may have the resources to incorporate such
a framework and the separate dimensions successfully. However,
according to the Chamber of Commerce, only 15% of the SMEs
in The Netherlands has heard of the term ‘Smart Industry’ or
‘Industry 4.0’ whilst most of the organisations in the Netherlands
Figure 1: Smart Industry Cyber-Physical System
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are labelled as an SME; up until 250 employees (Smetsers,
2016). Moreover, on the technical level, competencies in ICT,
automation technology & electronic and software engineering
are needed to develop or successfully use cyber-physical
systems. On the organisational and business level the
development of employee training and new business models are
especially demanding for SMEs (Jäger, Schöllhammer,
Lickefett, & Bauernhansl, 2016). Therefore, given the fact that
SMEs are important drivers for the economy and technological
change, the central research question will focus on this group.
Moreover, it will encompass three different bachelor theses and
will work towards multiple research goals. Firstly, the research
aims at broadening the understanding of Smart Industry and its
three dimensions. With understanding we mean what kind of
impact Smart Industry currently has on organisations. Secondly,
the research targets to lessen the gap between expectations and
the reality of Smart Industry. Thirdly, it is intended to observe
the SMEs industries’ current state of adoption Smart Industry.
3. THEORETICAL FRAMEWORK In this section of the research, it will go into depth of the first
layer of Smart Industry Cyber-Physical System. It analyses the
relevant literature on the different theories used in this research.
Firstly, to further understand this research paper, it will discuss
about what big data is and the ongoing importance. Secondly, the
literature on the evolution of analytics will be reviewed and
highlight how data and analytics go hand-in-hand. Lastly, the
maturity of level frameworks by R. Grossman, W. Eckerson and
Waston et. al will elaborated along with its main factors to
identify the maturity levels.
3.1 Big data In the recent years, from academics to corporations, big data are
the subject of attention and even to the extent, fear. It has quickly
evolved into a "hotspot” that entices attention from academia,
industry, and even governments (Jin, Wah, Cheng, & Wang,
2015). Many were left unprepared as big data suddenly rose.
Before, the developments of any “new” technology appeared first
in technical and academic journals. Later on, the knowledge and
production were published into other ways of knowledge
mobilization (including books). The swift evolution of big data
technologies and the acceptance rate by the private and public
sectors left little time for the converse to develop and mature in
the academic domain (Gandomi & Haider, 2015). Even
McKinsey, the renowned consulting firm, claimed that the big
data has grasped into every area of the industry and business
functions and has become a crucial factor in production (James
Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard
Dobbs, Charles Roxburgh, 2011). Using and mining data
prefigures a new wave of consumer motivation and growth of
productivity.
What is “big data”? It seems to entice and scare people in the
private and public sectors from every area. There is no
universally accepted definition (Jin et al., 2015). For IBM, big
data is “a term applied to data sets whose size or type is beyond
the ability of traditional relational databases to capture, manage,
and process the data with low-latency” (IBM, 2018). Another
definition of big data, given by TechAmerica Foundation, it
“describes large volumes of high velocity, complex and variable
data that require advanced techniques and technologies to enable
the capture, storage, distribution, management, and analysis of
the information” (Agarwal et al., 2012). To understand how
difficult it was and is to pinpoint the definition, please refer to
figure 2 as there was an online survey conducted by SAP that
showed how different executives had a different understanding
of what big data is (definitions ranging to what its functions are
to what big data is) (Interactive, 2012).
Size is a significant characteristic when concerning the question
“what big data is?” Over the years, other characteristics of big
data have surfaced and these characteristics are Volume, Variety
and Velocity (the Three Vs) (Laney, Management, & Volume,
2005) and thee Three V’s have been used as a common
framework to describe big data. First, Volume refers to the scale
of the data and is stated in multiple terabytes and petabytes. The
term of big data volumes is comparative and vary due to factors
such as time and type of data. Because it is accepted as big data
in present day does not mean that it will meet the threshold in the
future as the storage capabilities will increase, enabling even
bigger data sets to be obtained (Gandomi & Haider, 2015). It is
worth mentioning that the type of data and the type of industry
influence the definitions thus, it is unrealistic to determine a
specific threshold for big data volume. Second, Variety is about
the structural heterogeneity in a data set and as technologies
becomes advance, it allows firms to use various types of
structured, semi-structured and unstructured data (Gandomi &
Haider, 2015). Structured data, which establishes only 5% of all
existing data (Cukier, 2010) and this data can be found in
spreadsheets or relational databases. Examples of unstructured
data are images, text, audio and video and unstructured data
denotes to the lack of organisation required by machines for
analysis. Semi-structured data is the in-between of the two data
structures mentioned above, the format does not follow the strict
criterions. Organisations have been hoarding unstructured data
from internal sources (e.g. historical data) and external sources
(e.g. social media). The emergence of new innovative data
management technologies and analytics have allowed
organisations to control the data in their business processes e.g.
facial recognition has allowed retailers to obtain data about the
age, gender and movement patterns of their customers which can
aid in decisions related to product promotions and placements
(Gandomi & Haider, 2015). Lastly, Velocity regards to the rate
at which data are generated and the speed it takes for the data to
be analysed. As digital devices such smartphones and sensors
evolve and are used everywhere, the rate of data creation is
unparalleled and the need for real-time analytics and evidence-
based planning grows (Laney et al., 2005). It does not have to be
the unconventional to conjure data as such, even usual retailers
generated high-frequency data, for example, Wal-Mart (an
American multinational retail corporation) process more than
one million transactions per hour (Cukier, 2010). This type of
data, from smartphones and smartphone apps that produces
information that can be used to generate real-time, personalised
offers, can provides information about the customers that can be
analysed to create real customer value.
Besides the three V’s, three other dimensions of big data have
been discovered and they are Veracity, Variability and Value.
Figure 2: Definitions of big data based on an online survey
of 154 global executives in April 2012
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Veracity was pegged as the “fourth” V by IBM (IBM et al., 2018)
and this characteristic represents the unreliability in some
sources of data e.g. customer sentiments in social media as they
are biased due to human judgement. This can be addressed by
using tools and analytics developed for management and mining
of uncertain data. Variability (and complexity) was the additional
characteristics to be discovered by SAS (SAS, 2018b), variability
represents the variation in the data flow rates as the velocity of
big data is not always consistent and have periodic peaks and
dips. The complexity signifies that big data are generated through
numerous sources. Thus, this enforces a confrontation: the need
to connect, match, cleanse and transform data received from
various informants (SAS, 2018b). Value is often used as a
defining attribute of big data and that “big data are often
characterised by relatively low value density” (Oracle, 2014).
This means that the data collected in the original form has a low
value relative to its volume but analysing large volumes such
data, high value can be gained.
Big data is a by-product of the information era, an era that “has
been marked by increasingly pervasive digital technologies that
have reconstituted organisational life and action” (Caesarius &
Hohenthal, 2018), that is sustained by everyday generation,
storage and distribution of ample sets of data, in varied formats,
at extreme velocity and with increasing granularity. Due to the
abundance of data historically, it can be traced back to the
overview of “compatible and interoperable digital mediating
technologies” (Kallinikos, 2010). First, the process of
digitisation that has been constant for years due to the
combination of IT and more advanced database technologies in
organisations (Zammuto, Griffith, Majchrzak, Dougherty, &
Faraj, 2007). Second, the development and arrival of the Internet
when “digitisation entered in a first wave the personal sphere and
instigated social and cultural changes” (Kjaerulff, 2010). Third,
the introduction of social media “when digitisation entered in a
second wave the personal sphere and permeated the everyday
lives of people” (Bruns, 2008). Fourth and lastly, the influx of
network-connected objects that operate without human
involvement (known as Internet of Things) (Caesarius &
Hohenthal, 2018). In short, big data is an occurrence that disrupts
from traditional ways of dealing with data in organisation and
proposes a new way of knowledge production that utilises
“computational manipulation of complex data sets with
algorithmic accuracy to generate insights that were previously
deemed impossible” (Boyd & Crawford, 2012).
3.2 Analytics and its evolution By definition, according to SAS, analytics is “an encompassing
and multidimensional field that uses mathematics, statistics,
predictive modelling and machine-learning techniques to find
meaningful patterns and knowledge in recorded data” (SAS,
2018a). Analytics is a combination of processes and tools that
include statistics, data mining, artificial intelligence and
language processing (Russom, 2011). It is also used to large and
disperse datasets for gaining invaluable understandings to
improve firm decision making (Ertemel, 2015). Analytics is a
critical organisational IT competency due to the increased
amounts, speed of change and types of data in business over the
two decades (Kambatla, Kollias, Kumar, & Grama, 2014). There
is a pressure of firms needing to improve their data competency
to determine better, more informed and faster decisions. Recent
studies show the many firms that invest in data analytics do not
take full advantages of using the data analytics tools, even though
there is evidence that using data analytics help organisations
improve their decision-making performance. A report from
Deloitte claims that “only 25% of firms reported that analytics
has significantly improved their organisation’s outcomes”
(Deloitte, 2013) and another report claims that they found “only
27% of firms that invested in data analytics reported their
initiatives as successful” (Colas, Nambiar, Finck, Singh, &
Buvat, 2014). There are several arguments regarding data
analytics and its features. One could argue that organisations
need to concentrate on various confrontations to gain the
benefits, even if data analytics has immense potentials for
improving firms results (Akter & Wamba, 2016) whereas another
argues that even if a firm is investing in their data analytics, it
does not guarantee that it will improve their decision making and
different firm can play critical roles in successfully using these
tools (Ghasemaghaei, Hassanein, & Turel, 2017). Furthermore,
there is another argument that high failure rate of productively
using data analytics because of the obligatory required conditions
to generate understandings from analytics are unkempt as most
firms solely focus on data aspects such as data volume to
understand (Wu, Buyya, & Ramamohanarao, 2016). Therefore,
it is necessary to empirically study the characteristics of
successful data analytics initiatives.
Over the decades, analytics has evolved tremendously and
rapidly. To fully understand analytics and this research paper,
this part of the section will explicate about the evolution of
analytic technologies and applications that were and is adopted
in the industry. There are three groupings of the BI&A (Business
Intelligence and Analytics) are BI&A 1.0, BI&A 2.0 and BI&A
3.0 (please refer to figure 3) (Chen, Chiang, & Storey, 2012)
Figure 3: Evolution of BI&A
Beginning with BI&A 1.0, it is mostly where data are collected
by companies through various computer systems and often stored
in RDBMS (relational database management systems). The
techniques generally used in these systems commercialised
during the 1990s were mainly in statistical methods that were
established in the 1970s and the data mining was developed in
the 1980s. The key foundation of BI&A 1.0 are data management
and warehousing. Essential for transfiguring and incorporating
enterprise-specific data are data marts and tools for ETL
(extraction, transformation and load). Simple but insightful
graphics are used to discover important data characters such as
database query, OLAP (online analytical processing) and
reporting tools. BPM (Business performance management)
utilise scorecards and dashboards to analyse and visualise variety
of performance metrics. Statistical analysis and data mining
techniques are implemented for association analysis, data
segmentation and clustering, classification and regression
analysis, anomaly detection, and predictive modelling in various
business applications (Chen et al., 2012).
BI&A 2.0 began during the early 2000s when the Internet and the
Web began to offer different data collection and analytical
research and development prospects. Web intelligence, web
analytics and the user-generated obtained through Web 2.0-based
social and crowd systems have been piloted the BI&A 2.0
research in the 2000s (focusing on text and web analytics for
unstructured web contents) (Doan, Ramakrishnan, & Halevy,
2011; O’Reilly, 2007). It was the beginning of organisations
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were allowed to present their businesses online and interact with
their customers directly. For example, the introduction of using
delegated IP-specific user search and interaction logs that are
collected through cookies and server logs suddenly became an
important source in understanding customers’ needs and
recognising new opportunities e.g. using Google Analytics. The
BI&A 2.0 systems, unlike BI&A 1.0, “require the integration of
mature and scalable techniques in text mining…, web mining,
social network analysis and spatial-temporal analysis with
existing DBMS-based BI&A 1.0 systems” (Chen et al., 2012).
While BI&A 2.0 enticed research from the industry and
academia, BI&A 3.0 emerged with more research opportunities.
As mobile phones, tablets, are becoming more integrated into
everyday lives compared to the use of laptops and PCs, it is
predicted that “the number of connected devices would reach 10
billion in 2020” (The Economist, 2011). As mobile devices (e.g.
smart phones, anything that is capable of downloadable
applications) are affecting different parts of society from
healthcare to entertainment. The decade of the 2010s
foreshadows an era for high impact BI&A research development
for the industries and academia (Chen et al., 2012).
Figure 5: Evolution of Analytics
Using figure 4 (Chen et al., 2012) to explain the evolution of
analytics. First, data analytics signifies to the BI&A technologies
that are founded in data mining and statistical analysis. Most of
these methods are “data-driven, relying on various
anonymization techniques, while others are process driven,
defining how data can be accessed and used” (Gelfand, 2011).
Second, text analytics has transpired since the early 1990s when
search engines have evolved into mature commercial systems
that entailed of fast data from search logs analytics and link-
based page ranking. This signifies to the BI&A 1.0 development
in the text-based enterprise search and document management
systems (Chen et al., 2012). Third, web analytics developed as
an active field of research within BI&A and it offers exceptional
analytical challenges and opportunities e.g. statistical analysis
foundations of data analytics. Associated web search engines and
directory systems “helped develop unique Internet based
technologies for website crawling/spidering, web page updating,
web site ranking and search log analysis” (Chen et al., 2012).
Fourth, network analytics is an emerging research area that
advanced with contributions from computer scientists and
sociologist alike, since the early 2000s, that evolved to include
new computational models for online community and social
network analysis (Chen et al., 2012). Fifth and final, mobile
analytics is another radical research in different areas (e.g. crowd
sourcing, personalisation and behavioural modelling for apps)
and this method developed due to the increasing number of smart
phone and tablet owners globally, as an “an effective channel for
reaching many users and as a means of increasing the
productivity and efficiency of an organization’s workforce”
(Chen et al., 2012).
3.3 Maturity level frameworks
3.3.1 The TDWI Maturity Model by W. Eckerson This maturity model examines the characteristics of mature BI
implementations and to give organisations goals and motivation
to surmount the obstacles regarding BI implementation. Please
refer to figure 5 (Eckerson, 2007) to understand the descriptions
of each stage in the model.
1) Prenatal stage: Established operational reporting
system with a standard set of static reports; reports are
built into operational systems and limited to that
individual system (Eckerson, 2007; Hribar, 2010)
2) Infant stage: Faced with numerous partial data sources
called “spreadmarts” (spreadsheets/desktop databases
used as replacement for regional data) (Eckerson, 2007;
Hribar, 2010)
3) Child stage: Knowledge workers join community of BI
users, companies buy their first interactive tool and
capable of analysing trends & past data; it focused on
understanding the correlation in data and to gain
understanding of past business actions (Eckerson, 2007;
Hribar, 2010)
4) Teenager stage: Recognising the need and starts to use
a standardised set of project and development
methodologies (including best practices, learning past
experiences and extensive use of external consultants.
Software solutions developed on common data model
using common consolidation platform and recognises
value of consolidating regional DW into centralised
DW; use of BI is spread among regular users and
enables knowledge workers interactive reporting and
analysis (Eckerson, 2007; Hribar, 2010)
5) Adult stage: Developing from tactical to strategic
business level and BI becomes the central IT system
during daily operations IT system driving daily
operations of company; there is a generic architecture of
data warehouse, fully loaded with data and more
(Eckerson, 2007; Hribar, 2010)
6) Sage stage: The companies at this level are turning BI
system capabilities into technical and business services
and are moving development back to basic
organisational units through COE (Centres of
Excellences) (Eckerson, 2007; Hribar, 2010)
3.3.2 Analytic Processes Maturity Model (APMM)
by R. L. Grossman This maturity model is based off a framework for analytics that
divides analytics process into six areas: building analytics
models, developing analytic models, managing analytic infra-
structure, operating an analytic governance structure, providing
security and compliance for analytics assets and lastly,
developing an analytic strategy (Grossman, 2018a). This
maturity model is divided into five different levels/stages.
1) AML 1 – Builds report: “Organization can analyse data,
build reports summarizing the data, and make use of the
reports to further the goals of the organization”
(Grossman, 2018a)
Data Analytics
Text Analytics
Web Analytics
Network Analytics
Mobile Analytics
Figure 4 - The TDWI Maturity Model
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2) AML2 – Build models: “Organization can analyse data,
build and validate analytic models from the data, and
deploy a model” (Grossman, 2018a)
3) AML 3 – Repeatable analytics: “Organization follows a
repeatable process for building, deploying and updating
analytic models. In our experience, a repeatable process
usually requires a functioning analytic governance
process” (Grossman, 2018a)
4) AML 4 – Enterprise analytics: “Organization uses
analytics throughout the organization and analytic
models in the organization are built with a common
infrastructure and process whenever possible, deployed
with a common infrastructure and process whenever
possible, and the outputs of the analytic models
integrated together as required to optimize the goals of
the organization as a whole. Analytics across the
enterprise are coordinated by an analytic governance
structure” (Grossman, 2018a)
5) AML 5 – Strategy-drive analytics: “Organization has
defined an analytic strategy, has aligned the analytic
strategy with the overall strategy of the organization,
and uses the analytic strategy to select appropriate
analytic opportunities and to develop and implement
analytic processes that support the overall vision and
mission of the organization” (Grossman, 2018a)
3.3.3 Data Warehouse Stages by Watson et al.
This maturity level is built on off nine different variables (data,
architecture, stability of production environment, warehouse
staff, users of warehouse, impact on users’ skills and jobs, use of
the warehouse, organisational impacts and cost and benefits) to
help describe the different stages.
1) Initiation: There is limited amount of data for a single
or a few subjects areas; analysts in the business unit are
served by the data mart; reports and redefined & ad joc.
queries (backward looking to what has occurred)
(Watson, Ariyachandra, & Matyska, 2001)
2) Growth: There is data for multiple subject areas; users
from all of the business are served by the data marts,
diverse in the information needs and computer skills;
the reports and predefined are more about the analysis
of why things occurred and “what if” analyse for future
scenarios (Watson et al., 2001)
3) Maturity: There is data for enterprise -wide (well-
integrated and for multiple time periods); users from the
whole organisation can access to the warehouse data;
there are reports, predefined queries, DSS and EIS, data
mining provides predictive modelling capabilities;
integration with operational systems (Watson et al.,
2001)
4. RESEARCH QUESTION
4.1 Overall research question Overall, as a circle group, our research question is:
“What is the impact of Smart Industry technologies on SMEs in
the Netherlands?”
4.2 Specific Research question From the overall research question, we each specified our own
question that will help answer the question above:
“To what extent are current frameworks of maturity levels of
analytics suitable to SMEs?
And the following sub-questions are:
“How to identify what certain aspects could be improved on for
SMEs?”
“How SMEs can have a better understanding on how to improve
on certain aspects?”
5. METHODOLOGY This study presents an exploratory research conducted for the
purpose of obtaining an understanding on whether the maturity
level frameworks are suitable for SMEs to use and the certain
aspects that make the difference. Through this research,
understanding of the difference between what is anticipated and
what is unanticipated from the maturity level frameworks can be
gained. The analysis is based on qualitative and quantitative
research focusing on the analytics used in SMEs in the
Netherlands. The SMEs examined within this study mostly lies
on the industrial and manufacturing (depending how successful
the data collection due to time constraints).
5.1 Approach For this research, my main question is “to what extent are the
current frameworks of maturity levels of analytics suitable to
SMEs?” What is meant with ‘suitable’ is to understand how
SMEs can identify certain improvements aspects that is relevant
to them by using the analytic maturity frameworks and to see
how they can actually improve their analytics. Thus, to help
understand and collect the correct data, we have to first establish
the concept of maturity of analytics and the evolution of
analytics. First, what is the maturity of analytics? There are
several frameworks found that help identifies where companies
current state in regards to how they interact and use their data.
These frameworks are made by companies such as SAS and
researchers like Watson et. al. (Watson, Ariyachandra, &
Matyska, 2001, p. 42) and Robert L. Grossman (Grossman, 2018,
p. 45) alike. The maturity means about how much a company
embraces analytics and how sophisticated the analytics is to gain
better revenue. In short, across many literature papers and
research, the main idea correlated throughout is that that the more
‘mature’ the company is in regards to their analytics, the
“financial and general support for BI management and business
functions have a positive impact on the overall organizational
performance” (Lahrmann, Marx, Winter, & Wortmann, 2011, p.
7) and “it can have a transformative effect on the organization of
work in the firm” (Caesarius & Hohenthal, 2018, p. 10). Second,
what is the evolution of analytics? It is the change in the
characteristics of analytics over the last five decades thus, BI&A
1.0 (DBMS-based and structured content), BI&A 2.0 (web-based
and unstructured content) and lastly, BI&A 3.0 (mobile and
sensor-based content) (Chen et al., 2012). Thus, it is of the
essence to find out whether the evolution stages of analytics held
accountable for SMEs in the Netherlands.
Figure 6: Stages of Growth
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5.2 Subjects of Study Concerning the subjects of the study, there are two main group
of subjects that can be recognised: the SMEs in the region of
Twente and SMEs in the other regions of the Netherlands. The
delegates of the firms, such as directors and employees, are
anticipated in providing their knowledge of how the firm
practices their data, the outcome of their findings and the certain
type of analytics used. The possibility is given to collect data as
SMEs play an important role in Overijssel’s economy and also
Twente, as 25% of employment is situated in companies with
fewer than ten employees and 60% in companies with fewer than
100 employees (The European Commission, 2018). Due to the
lack of time and access, it is not feasible to directly approach all
SMEs in the region. Directors and managers were chosen the
basis of their relevance to my research subject and their position
within the firm. This is because of their years of experience in
their position and direct knowledge of analytics and data. It must
be noted that their assumption and beliefs can influence the
outcomes that can lead to biased results. Other regions of the
Netherlands were chosen as the second subject of the study as it
is known that the Netherlands has a strong SBA profile (Small
Business Act for Europe) compared to other EU countries as it
ranks above the average (The European Commission, 2017). It
must be noted that the Netherlands in particularly strong in the
“entrepreneurship, ‘second chance’, ‘responsive administration’
and skills & innovation…in entrepreneurship the country topped
all other EU Member States” (The European Commission, 2017).
5.3 Data collection As we want to see what the impact of Smart Industry is
throughout the Netherlands, we have agreed together to send a
questionnaire to a variety of companies. This may not be enough
and thus; we can still do two in-depth interviews. However, the
focus now lies on gathering enough data through the
questionnaire in order to make ‘Smart Industry in the
Netherlands’ representative and reliable. The questionnaire is
sent to SMEs in the area of Twente and in the Netherlands that
have a manufacturing process of some kind. The contacts are
gathered by calling the organisations and searching for their e-
mail address on their website. To ensure that the data is kept
confidential, we only share the results connected with the
company names between the three individual researchers. In the
results, no names will be used, just the outcomes of the
measurements. The questionnaire will specify this and
respondents always have the choice throughout the questionnaire
not to join the research. Moreover, if the respondents have any
questions and remarks, they have the choice to e-mail the
researchers and/or the supervisor F. Wijnhoven at any time. To
ensure that our questionnaire is representative, we will have to
have a certain number of respondents. The number of individuals
in the sample size is all that matters. According to de Veaux
(2015), “for a questionnaire that tries to find the proportion of the
population falling into a category, you’ll usually need several
hundred respondents to say anything precise enough to be useful
(p. 313)”. Thus, a larger sample will make our results more
precise. Therefore, we target for 100 organisations. As for our
questions that we will ask in the questionnaire, “results depend
crucially on the questionnaire that scripts this conversation
(irrespective of how the conversation is mediated, e.g., by an
interviewer or a computer). To minimize response errors,
questionnaires should be crafted in accordance with best
practices (Krosnick & Presser, 2010).” Especially within the
field of Smart Industry with specific terms, it quite difficult to
make the questionnaire one hundred per cent understandable.
However, to make it as good as possible, when making our
questionnaire, we included the following best practices: 1)
Avoiding double-barrelled questions. 2) Using simple jargon for
words that are difficult. 3) Avoiding questions with single or
double negations. 4) Short questions. 5) Avoiding leading or
loaded questions that push respondents toward an answer. 6)
Ensuring that everyone interprets the words/sentence in the same
way. In order to test our methodology in the questionnaire, we
have created an operationalisation framework (see figure 7)
which makes clear how our subjects and the concepts within the
subjects align with each other.
In order to measure the two concepts for my research question,
the maturity of analytics and evolution of analytics, we need to
operationalise the necessary variables. The maturity of levels is
from the following features and overall research (Watson,
Ariyachandra, & Matyska, 2001; Grossman, 2018; Eckerson,
2007):
1. Data
2. User
3. Application
4. Output
5. Operation
These variables are found as the general, overlapping indicators
that help identify which stage each an organisation is at and what
certain analytics is used, for example. Using these variable, it can
be operationalised to make it easier to be measured and obtained
through the questionnaire (please refer to the operationalisation
framework to see the operation to see how the variables were
operationalised). The evolution of the analytics will be
distinguished based on the features mentioned above and
literature review. If an organisation is using analytics such as
RDBMS, data warehousing and statistical analysis etc., it will be
categorised as “initiation stage” (Watson, Ariyachandra, &
Matyska, 2001). If an organisation is using analytics such as web
analytics, web intelligence and social media analytics etc., it will
be categorised as “growth stage” (Watson, Ariyachandra, &
Matyska, 2001). Lastly, if an organisation is using location-
aware analysis, person-centred analysis etc., it will be
categorised as “maturity stage” (Watson, Ariyachandra, &
Matyska, 2001).
Figure 7: Operationalisation Framework
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6. DATA ANALYSIS The following section will describe the outcomes of the data
collected from the questionnaires. It must be noted that Microsoft
Excel, IBM SPSS Statistic version 25 and Qualtrics were used to
analyse the data set (the results can be found in the appendix).
Please refer to the appendix for the results of the questionnaires
for the exact numbers as they were not referred due to page
restraints. Table 1 shows the number of SMEs that participated
in this research study and it must be reminded the number of
participants is relatively small compared to be what is expected
due to various issues (that will be mentioned in another section).
In order to gain an understanding of the result and to indicate
where the SMEs in the Netherlands are places in terms of the
three maturity level frameworks, this section will be divided into
the following: overall outcome of SMEs and outcome from size
of SME.
6.1 Overall outcome of SMEs Beginning with question 1, this question was about allowing the
SME representative to choose the following statements that felt
most relevant to them (refer to table 2) and this question was
constructed from Grossman’s Analytic Processes Maturity
Model, with the intent of being able to indicating where the SME
stood in this spectrum. Table 2 shows the statements that most of
the SMEs selected and this indicates the most of the SMEs fall
into the category of AML 1 and AML 2 (Grossman, 2018a).
Moving on with question 2 until question 6, these questions were
constructed from Eckerson’s TDWI Maturity Model and the
following questions are stated below (please refer to table 3).
Using these questions helped indicate where the overall SMEs
belonged on this maturity level framework.
Table 3 - Questions related to Eckerson
Questions
Question 2 Who uses the analytics in your company?
Question 3 What is your focus on analytics?
Question 4 What is the focus of using analytics?
Question 5 What is the outcome of using analytics?
Question 6 What are the tools used for analytics?
Overall, majority of the results from these questions prove that
most of the SMEs fall in the category of Infant and Teenager
depending on the certain question and functionalities (Eckerson,
2007).
Table 4 - Result of Question 2
Table 5 – Result of Question 3
Table 6 – Result of Question 4
Table 7 – Result of Question 5
Table 1 - Participants of the questionnaire (N=18)
Table 2 – Results of Question 1 (related to Grossman)
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Table 8 - Result of Question 6
Table 9 - Results of Question 2-6
Questions Answers Percent Result
Question 2 3 39.4% Teenager
Question 3 1 29.3% Infant
Question 4 3 31.8% Teenager
Question 5 4 28.3% Adult
Question 6 1 36.4% Infant
Ending with the final part with question 7 until 10, these
questions, like the other questions, were constructed from
Watson et al.’s Data Warehouse stages (refer to table 5. From the
results, it can derived that the majority of SMEs are in the
category of Initiation and Growth stage (Watson et al., 2001) and
from this, please refer to table 6 for the results (for the results of
question 10, please refer to the appendix).
Table 10 - Questions related to Watson et al.
Questions
Question 7 Do you use internal data to improve the
processes within the company?
Question 8 Do you use external data to create products
specified to the need of one group of people?
Question 9 Do you use data analytics that can help
generate useful data information catered
towards needs of company?
Question 10 In general, what kind of analytics do you use
in your company?
Table 11 - Results of Question 7-9
Questions Answers Percent Result
Question 7 Yes 39.4% Teen
Question 8 No 29.3% Infant
Question 9 Yes 31.8% Teen
Table 12 - Result of Question 7
6.2 Outcome from size of SME Interestingly, using SPSS to make custom tables with the size as
the independent variable and the other results from the questions
as the dependent variable helped understand the bigger picture of
the data set. From this data analysis, it showed how size has an
impact on the use of analytics. Due to page constraints, please
refer to the appendix to see the results of the questionnaire from
SPSS for the results compared with size.
First of all, using the results from question 1 and the sizes of the
SMEs, table 7 shows an overview of the size of SME and the
indication of Grossman’s Analytic Process Maturity Model.
Table 12 – Result of Question 1 with Size
Size of SME Result
2-50 AML 1
10 -50 AML 2
101-150 AML 4
151-200 AML 4
Second, using the results from question 2 until 6 and the sizes of
the SMEs, table 8 show an overview sizes of the SMEs and the
indication of the Eckerson’s TDWI Maturity Model.
Table 13 - Results of Question 2-6 with Size
Size of SME Result
2-50 Infant/Teenager
10 -50 Teenager
101-150 Teenager/Adult
151-200 Teenager/Adult
Lastly, using the results from question 2 until 6 and the sizes of
the SMEs, table 8 show an overview sizes of the SMEs and the
indication of the Eckerson’s TDWI Maturity Model.
Table 14 - Results of Question 7-9 with Size
Size of SME Result
2-50 Initiation stage
10 -50 Growth
101-150 Maturity
151-200 Maturity
7. DISCUSSION As mentioned before in the theoretical framework, there is no
universal accepted definition of “big data”, nonetheless, the
definition established by NIST is relevant for this section: “Big
Data refers to digital data volume, velocity and/or variety
[veracity] that: enable novel approaches to frontier questions
previously inaccessible or impractical using current or
conventional methods; and/or exceed the capacity or capability
of current or conventional methods and systems” (NIST, 2017).
As time goes by, analytic maturity is important for numerous
factors when it comes to big data. First, as the variety, velocity
and volume of the data develop, the importance of having
suitable analytic infrastructure expands (Grossman, 2018b).
Second, more multiple models will be probable to be used
Table 13 - Results of Question 8
Table 14 - Result of Question 9
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concerning big data and this is because the number of models
will continuously grow and it will become more essential to
“have an analytic infrastructure that can build, manage and
deploy these models” (Grossman, 2018b). Lastly, once big data
becomes recognised to organisation as valuable, models must be
built from the big data and can be implemented into “products,
services and operations to increase revenues, decrease costs,
reduce risk and optimise operations” (Grossman, 2018b). Hence,
it is imperative that the more maturity the analytics are, the more
likely these entities will happen.
Comparing the results for the overall outcome of question 1 and
the results with the size as an independent variable of question 1,
there are points worth noting. It must be noted that as the data set
is very small, it is infeasible to generalise the whole population
thus, it is critically discussed of what could have been the case
had the sample size been larger. First, the results of the overall
outcome show that most SMEs in the Netherlands could be either
on AML 1 and/or AML2. There is subtle difference with AML 1
and AML2, these AML 2 organisations can build models that
make predictions about future event rather than summarising past
event and comprehend the difference between “(business) rules
and analytics and integrate both of them into deployed systems”
(Grossman, 2018a). It could be recommended for most SMEs to
focus on their skills on setting up a model that can make
predictions about the future. Regarding the results with the
overall outcome with the size, SMEs that have a size of 2-50 are
assumed in the AML 1, SMEs that have a size of 10-50 are
assumed in the AML 2 and lastly, there is an exception for SMEs
with the sizes of 101-150 and 151-200 as it can be assumed that
they are in the AML 4. As AML 1 and 2 were discussed before,
AML organisations have defined their processes and structures
(including a governance structure); there is an uniform process
used across the organisation when choosing analytics
(Grossman, 2018a).
Next, examining the results for the overall outcome and with the
size as an independent variable for Eckerson’s framework (from
question 2 until 6). The results of the overall outcome help
assume that most SMEs in the Netherlands may be in the Infant
and Teenager stage. The SMEs on the Infant stage mainly may
use spreadsheets and desktop databases used as replacement for
regional data whereas the SMEs on the Teenager stage may
recognise the needs and starts using a standardised set of project
and development entities such as learning past experiences; also
using more sophisticated software compared to the software used
in the Infant stage. However, the end results are different when it
comes to size because most of the SMEs that have a size of 10-
50, 101-150 and 151-200 are assumed to be in the stage of
Teenager and Adult unlike the SMEs that have a size of 2-50 that
is on the Infant/Teen stage. In the Adult stage, the SMEs can do
everything from the Teenager stage and moreover, “BI becomes
the central IT system during daily operations IT system driving
daily operations of company” (Eckerson, 2007).
Lastly, the results from the overall outcome and with the size an
independent variable from the questions derived from Watson et
al. can be inspected. The results of the overall outcome assume
that most SMEs in the Netherlands may be in the Initiation stage
and Growth stage. This means that for the SMEs in the Initiation
stage, the analytics may be straightforward as there might be
limited amount of data available and rather than sophisticated
functions, it may focus on examining what has occurred (Watson
et al., 2001). While the SMEs in the Growth stage, the analytics
might be more sophisticated but accessible as there might be
more users that use it and more analysis on why things occurred
and “what if” scenarios for the future (Watson et al., 2001). On
the other hand, the results with the size as an independent
variable again show a different end result because SMEs with the
size of 2-50 may be in the Initiation stage, SMEs with the size of
10-50 may be the Growth stage and lastly, SMEs with the size of
101-150 and 151-200 may be both in the Maturity stage.
8. CONCLUSION From the several findings, there are improvements and
recommendations that could be made for the maturity level
frameworks (even when creating a new framework solely for
SMEs). Though the sample size is small and it cannot be used to
generalise the whole population, it can be deducted that size
plays a big influence as this was the case for all three frameworks
when the results were compared. In conclusion, the bigger the
SME, the more the SME use analytics to their advantage and can
utilise their data to the fullest. The size also has an effect on how
up-to-date the analytics are, for example, SMEs (that have the
size of 2-50) in the Infant stage may spreadsheets compared to
the SMEs (that have the size of 151-200) in the Adult stage that
may use cascading scorecards. Some improvements that could be
made that would make these frameworks more suitable and be
used as an improvement tool are 1) being clearer with what tools
are relevant for certain aspects, 2) making sure size is
incorporated, 3) using the size as the main constant, 4) rather than
generalising SMEs, categorising them into their industry as a
construction may not have any use of being recommended web
analytics compared to the food catering industry. Please refer to
the table as an example of this ideal framework, using the other
three maturity level of analytic frameworks.
Table 15 -Potential maturity model for SMEs (size)
Size of SME Levels Description
2-50 Birth Build reports
10 -50 Infancy Build models
101-150 Childhood Repeatable
analytics
151-200 Adolescence Enterprise
analytics
201-250 Adulthood Strategy-drive
analytics
9. LIMITATIONS OF RESEARCH The purpose of the research was to determine whether the
maturity level frameworks were of relevance to the SMEs in the
Netherlands and there are limitations of this research that should
be noted. First, this research requires additional empirical
evidence as it was not sufficient. It was, during the start, limited
to a specific region (Twente) which was later on expanded to the
whole of the Netherlands. This was because it was difficult to
collect even an appropriate amount needed for this research and
thus, it is essential to collect further research to classify a
considerable sample to generate a sounder framework and most
importantly, safeguard a greater reliability and validity of the
data. Second, this research requires a longer timeframe to be able
to collected the necessary amount of data needed and this
limitation goes hand in hand with the first limitation mentioned.
Third, the data collected were relied on the subjective viewpoints
of the SMEs representative and the representative may not have
had the knowledge required to answer certain questions
regarding, for example, knowing all the analytics used at the
company. Also, the representative may not have answered
truthfully in order to have better representation and indication on
the maturity level frameworks. Therefore, these limitations may
have led to biased results and conclusions. Lastly, the
administered questionnaire was not immaculate. This is because
there were a few technical difficulties and as most of the
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questions in the questionnaire was in English, there may have be
some misunderstanding, for example, in the terminology of
certain analytics and may have been clear for the SME
representative had the questionnaire been in Dutch.
10. ACKNOWLEDGEMENTS First of all, I would like to thank my supervisor, Dr. A.B.J.M.
Wijnhoven, for his guidance, supervision, and valuable feedback
while writing the bachelor thesis. Second, I would like to thank
my bachelor thesis circle, Robbin Baars and Johannes
Diederichs, for their feedback, support and teamwork throughout
the whole process of writing the thesis. Lastly, I would like to
thank my family and friends, from the bottom of my heart, for
their encouragement, love and endless support during my studies
and without whom I would have never been able to attain this
bachelor degree.
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12. APPENDIX
12.1 SPSS outcome (Overall)
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12.2 SPSS outcome (with size)
12.2.1 Question 1
12.2.2 Question 2
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12.2.3 Question 3
12.2.3.1 Question 4
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12.2.4 Question 5
12.2.5 Question 6
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12.2.6 Question 7, 8, 9
12.2.7 Question 10
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12.3 Questionnaire (with coding) Q2 Hoeveel werknemers heeft uw organisatie?
o 1 (1)
o 2-50 (2)
o 10-50 (3)
o 101-150 (4)
o 151-200 (5)
o 201-250 (6)
o Meer dan 250 (7)
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Q3 In welke sector bevindt uw organisatie zich?
o Landbouw, bosbouw en visserij (1)
o Winning van delfstoffen (2)
o Industrie (3)
o Productie en distributie van en handel in elektriciteit, aardgas, stoom en gekoelde lucht (4)
o Winning en distributie van water, afval- en afvalwaterbeheer en sanering (5)
o Bouwnijverheid (6)
o Groot- en detailhandel, reparatie van auto’s (7)
o Vervoer en opslag (8)
o Logies-, maaltijd- en drankverstrekking (9)
o Informatie en communicatie (10)
o Financiële instellingen (11)
o Verhuur van en handel in onroerend goed (12)
o Advisering, onderzoek en overige specialistische zakelijke dienstverlening (13)
o Verhuur van roerende goederen en overige zakelijke dienstverlening (14)
o Openbaar bestuur, overheidsdiensten en verplichte sociale verzekeringen (15)
o Onderwijs (16)
o Gezondheids- en welzijnszorg (17)
o Cultuur, sport en recreatie (18)
o Overige dienstverlening (19)
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Q4 De organisatie bevindt zich in de omgeving van Twente
o Ja (1)
o Nee, in de provincie... (2) ________________________________________________
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Q7 Please select the following statements that have relevance to your company
▢ Our company can analyse data (1)
▢ Our company can build reports summarising the data (2)
▢ Our company can make use of the reports to further the goals of the organisation (3)
▢ Our company can use the analysed data to build analytic models (4)
▢ Our company can use an analytic model (5)
▢ Our company follows a repeatable process for building analytic models (6)
▢ Our company follows a repeatable process for deploying analytic models (7)
▢ Our company follows a repeatable process for updating analytic models (8)
▢ Our company uses analytics throughout the company (9)
▢ Analytics models are built with a common infrastructure and process whenever possible (10)
▢ Analytics models are used with a common infrastructure and process whenever possible (11)
▢ The outputs of the analytic models integrate together to optimise goals of the company (12)
▢ Analytics across the enterprise are coordinated by an analytic governance structure (13)
▢ Our company has defined an analytic strategy (14)
▢ The analytic strategy aligns with the overall strategy of the company (15)
▢ Uses the analytic strategy to select appropriate analytic opportunities (16)
▢ Our company use the analytic strategy to select appropriate analytic opportunities (17)
▢ Our company use the analytic strategy to develop and implement analytic processes that support the overall
vision and mission of the company (18)
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Q8 Who uses the analytics in your company?
▢ Analyst (1)
▢ Knowledge worker (2)
▢ Manager (3)
▢ Executive (4)
▢ Customer (5)
▢ Other (6) ________________________________________________
Q9 What is your focus on using analytics?
▢ To make plans (1)
▢ To assign (2)
▢ To process (3)
▢ To make a strategy (4)
▢ To make services (5)
▢ Other (6) ________________________________________________
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Q10 What is the focus of using analytics?
▢ To know what will happen (1)
▢ To know why it happen (2)
▢ To know what is happening (3)
▢ To know what to do (4)
▢ To know what we can offer (5)
▢ Other (6) ________________________________________________
Q11 What is the outcome of using analytics?
▢ Plans (1)
▢ Procedures and policies (2)
▢ Caution (3)
▢ Action (4)
▢ o Recommendation (5)
▢ Other (6) ________________________________________________
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Q12 What are the tools used for analytics?
▢ Spreadsheets (1)
▢ Online analytical processing (2)
▢ Dashboard (3)
▢ Scorecards (4)
▢ o Statistical models (5)
▢ Other (6) ________________________________________________
Q13 Do you use internal data to improve the processes within the company?
o Yes (1)
o No (2)
Q14 Do you use external data to create products specified to the need of one group of people?
o Yes (1)
o No (2)
Q15 Do you use data analytics that can help generate useful data information catered towards needs of company?
o Yes (1)
o No (2)
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Q16 In general, what kind of analytics do you use in your company?
▢ RDBMS & data warehousing (1)
▢ ETL (process in data warehousing responsible for pulling data out of the source systems and placing it
into a data warehouse) (2)
▢ OLAP (approach to answering multi-dimensional analytical queries swiftly in computing) (3)
▢ Dashboards & scorecards (4)
▢ Data mining & statistical analysis (5)
▢ Information retrieval and extraction (6)
▢ Opinion mining (7)
▢ Question answering (8)
▢ Web analytics and web intelligence (9)
▢ Social media analytics (10)
▢ Social network analysis (11)
▢ Spatial-temporal analysis (analysis of large spatiotemporal, space and time, databases) (12)
▢ Location-aware analysis (services are tied to mobile networks, mobile analytics) (13)
▢ Person-centered analysis (proposes focusing on the individual (14)
▢ Context-relevant analysis (15)
▢ Mobile visualization (16)
▢ HCI (focused on the interfaces between people (users) and computers) (17)
▢ Other (18) ________________________________________________